Design and electrical simulation of on-chip neural learning based on nanocomponents

Concepts

EngineeringComputer EngineeringElectrical EngineeringNanotechnologyReconfigurable ComputingSemiconductor DevicesNanoelectronicsNeural Circuit ModelingNeural CircuitsNeural NetworksCarbon Nanotube Field-effect TransistorsLookup Table ArchitecturesLookup Table DesignNeural Circuit ArchitectureNeuromorphic DevicesNeuromorphic HardwareReconfigurable ArchitecturesReconfigurable Circuits

Abstract

A neural inspired lookup table for reconfigurable circuits is described and simulated. The design is based on conductive bridge RAM to implement the synapses and carbon nanotube field effect transistors (CNTFET) for the other parts. Electrical simulations demonstrate compatibility between the nanocomponents and show the successful training of a linearly separable logical function NOR3.

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